Lung Cancer Treatment Advances in 2022

Rous, Fawzi Abu; Singhi, Eric K.; Sridhar, Arthi; Faisal, Muhammad Salman; Desai, Aakash

doi:10.1080/07357907.2022.2119479

Cited by 41 publications

(20 citation statements)

References 28 publications

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“…[1] Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all diagnoses based on the histopathological type. [2] Treatment of NSCLC is exceptionally complex due to the presence of concurrent genomic alterations, [3] metabolic reprogramming, [4] and epigenetic abnormalities. [5] Hypoxia is a hallmark feature of solid tumors such as NSCLC.…”

Section: Introductionmentioning

confidence: 99%

The Interplay Between HIF‐1α and EZH2 in Lung Cancer and Dual‐Targeted Drug Therapy

Wang,

Yang,

et al. 2023

Advanced Science

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Interactions between oncogenic proteins contribute to the phenotype and drug resistance. Here, EZH2 (enhancer of zest homolog 2) is identified as a crucial factor that mediates HIF‐1 (hypoxia‐inducible factor) inhibitor resistance. Mechanistically, targeting HIF‐1 enhanced the activity of EZH2 through transcription activation of SUZ12 (suppressor of zest 12 protein homolog). Conversely, inhibiting EZH2 increased HIF‐1α transcription, but not the transcription of other HIF family members. Additionally, the negative feedback regulation between EZH2 and HIF‐1α is confirmed in lung cancer patient tissues and a database of cell lines. Moreover, molecular prediction showed that a newly screened dual‐target compound, DYB‐03, forms multiple hydrogen bonds with HIF‐1α and EZH2 to effectively inhibit the activity of both targets. Subsequent studies revealed that DYB‐03 could better inhibit migration, invasion, and angiogenesis of lung cancer cells and HUVECs in vitro and in vivo compared to single agent. DYB‐03 showed promising antitumor activity in a xenograft tumor model by promoting apoptosis and inhibiting angiogenesis, which could be almost abolished by the deletion of HIF‐1α and EZH2. Notably, DYB‐03 could reverse 2‐ME2 and GSK126‐resistance in lung cancer. These findings clarified the molecular mechanism of cross‐regulation of HIF‐1α and EZH2, and the potential of DYB‐03 for clinical combination target therapy.

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Section: Introductionmentioning

confidence: 99%

The Interplay Between HIF‐1α and EZH2 in Lung Cancer and Dual‐Targeted Drug Therapy

Wang,

Yang,

et al. 2023

Advanced Science

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“…Lung cancer was the primary cause of 158,000 fatalities in the United States 2 , accounting for 28% of cancer deaths among men and 26% of cancer deaths among women. Little has improved in this area during the preceding 50 years, and lung cancer still has one of the lowest 5-year survival rates of any malignancy, at 17.7% 3 . The prognosis is poor since most lung cancers manifest at an advanced stage.…”

Section: Introductionmentioning

confidence: 99%

Integrated genomic analysis of lung squamous cell carcinoma subtypes characterized by immunogenic cell death-relevant gene signature

Wang,

Ding

et al. 2023

Preprint

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Purpose A kind of regulatory cell death known as immunogenic cell death (ICD) can trigger anti-tumor immunity and aid in the adjustment of the tumor microenvironment (TME). Due to the very diverse and invasive nature of lung squamous cell carcinoma (LUSC), the identification of the immunogenic cell death related biomarkers for the distinction and prognosis of LUSC subtypes is essential for its therapy. Patients and methods 504 LUSC samples' rna sequencing data was reviewed in our investigation. Unsupervised clustering methodology was used to examine the distinctive pattern of gene expression in the tumor microenvironment for genes associated to immunogenic cell death. A number of ICD-related subtypes and clinical traits are connected to the effectiveness of immunotherapy through these expression patterns. The presence of the immunological status and prognostic characteristics is then confirmed using the test data set. Results Through the consistent clustering of ICD-related gene expression profiles, subtypes with significant differences in immune score, immune cell infiltration level and prognosis survival were found in the two groups. At the same time, the prognosis prediction model of patients was constructed according to the differentially expressed immunogenic cell death-related genes, which can provide guidance for clinical treatment. Conclusion This research developed a prognostic model of LUSC with ICD-associated genes, which will help us better understand ICD and determine how to manage LUSC patients.

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“…Early detection and treatment of lung adenocarcinoma can improve pa tients' survival rate and quality of life. Accurate diagnosis can also avoid misdi agnosis and missed diagnosis, thus preventing unnecessary surgery and treatmen t [4] Lung cancer can be diagnosed using various imaging methods, and histopat hology diagnosis is one of the most accurate and precise diagnostic methods in clinical practice. Artificial inspection of lung abnormalities based on histopath ological images is a time-consuming process requiring experienced pulmonologi sts and experts to do a vast amount of work for classification and identificatio n.…”

Section: Introductionmentioning

confidence: 99%

Multi-classification-assisted diagnosis of multi-scale lung adenocarcinoma pathological maps based on Scalenet101

Huang,

Li,

Yang

et al. 2023

Preprint

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Among all types of cancers, lung cancer is the deadliest cancer with a high mortality rate. Early diagnosis of lung cancer enables its timely and effective treatment, which can help reduce the risk of death. The aim of this research is to propose a deep learning approach for classifying and detecting lung adenocarcinoma tissues. The method was an improved Scalenet101 based on class activation mapping. (I) Scalenet101 classified lung adenocarcinoma tissue images into cancer, stromal and normal categories with the help of the Sigmoid. Then, the performance of Scalenet101 was validated using the pre-trained downstream techniques such as AlexNet, VGG16, VGG19, and ResNet50. (ii) Multi-scale fusion and weight fitting were performed to obtain multi-classification activation maps, which could improve the interpretability and accuracy in lung cancer evaluation. The performance of this model was tested using benchmark lung adenocarcinoma tissue images from WSSS4LUAD. The results showed that the model achieved an absolute classification accuracy of over 93.86% and an F1 score of over 95.44%.

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Lung Cancer Treatment Advances in 2022

Cited by 41 publications

References 28 publications

The Interplay Between HIF‐1α and EZH2 in Lung Cancer and Dual‐Targeted Drug Therapy

The Interplay Between HIF‐1α and EZH2 in Lung Cancer and Dual‐Targeted Drug Therapy

Integrated genomic analysis of lung squamous cell carcinoma subtypes characterized by immunogenic cell death-relevant gene signature

Multi-classification-assisted diagnosis of multi-scale lung adenocarcinoma pathological maps based on Scalenet101

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